Distributionally Robust Geometric Joint Chance-Constrained Optimization: Neurodynamic Approaches

arXiv:2603.06597 · cs.NE, cs.AI, math.OC, math.PR · Submitted 2026-02-10 · Read on arXiv

cs.NE, cs.AI, math.OC, math.PR

Submitted: 2026-02-10

Updated: 2026-03-14

Comments: Metadata update, error in arXiv categories

Journal ref: Valli, A., Tassouli, S., & Lisser, A. (2026). Distributionally robust geometric joint chance-constrained optimization: Neurodynamic approaches. Computers & Operations Research, 107484

DOI: 10.1016/j.cor.2026.107484

Code: https://github.com/philipperemy/cond_rnn

Project page: https://angevalli.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Robustness — 4× in this paper · explained in 5 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Lyapunov function — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Physics-Informed Neural Networks (PINNs) — 1× in this paper · explained in 6 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes

Related papers

• Evolutionary Ensemble of Agents
• Encoding and Decoding Temporal Signals with Spiking Bandpass Wavelets
• Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systems
• Learning Alzheimer's Disease Signatures by bridging EEG with Spiking Neural Networks and Biophysical Simulations
• Investigating Hyperparameter Optimization and Transferability for ES-HyperNEAT: A TPE Approach
• S-AI-Recursive: A Bio-Inspired and Temporal Sparse AI Architecture for Iterative, Introspective, and Energy-Frugal Reasoning